alexnet in keras

channels) in the 16 feature map of the convolution operation, the 16 channels of information fusion (with 1x1 convolution for the information fusion between different channels), We say that this step is pointwise (per pixel). So we can figure out the whole process using 3x3x16+ (1x1x16) x32 = 656 parameters. 1.3 Advantages of deep separable convolution It can be seen that the required parameters are reduced by the use of deep separable convolution than the ordinary convolution. It is important

* Height * Width, such as 256*3*224*224. to the conv layer. Weight BLOB size is the number of Output nodes * Input nodes * Height * Width, such as alexnet the first conv layer has a blob size of 3 x 11. for the inner product layer, the weight blob size is 1 * 1 * The number of OUTPUT nodes * input node, bias blob size is 1 * 1 * 1 * Output node number (conv layer and inner Product layer. There are also weight and bias, so in the defin

NIPS 2016 article: Intel China Research Institute on Neural Network compression algorithm of the latest achievementsHttp://www.leiphone.com/news/201609/OzDFhW8CX4YWt369.htmlIntel China Research Institute's latest achievement in the field of deep learning--"dynamic surgery" algorithm 2016-09-05 11:33 reproduced pink Bear 0 reviewsLei Feng Net press: This article is the latest research results of Intel China Research Institute, mainly introduces a "dynamic surgery" algorithm, which effectively sol

).Textbook-style code, understanding and understanding can help you implement models by yourself in the future. Run the model, debug, and adjust parameters. After reading the entire project logic of MNIST or CIFAR10, you can master the TensorFlow project architecture.Slim directory. TF-Slim image classification library. Defines, trains, and evaluates lightweight advanced APIs for complex models. Training, evaluation lenet, alexnet, vgg, inception_v1,

)* * Part One: Caffe command-line parsing * * ————— First, training model code Script:./build/tools/caffe train -solver models/finetune/solver.prototxt -weights models/vgg_face_caffe/VGG_FACE.caffemodel -gpu 0BAT Command:..\..\bin\caffe.exe train --solver=.\solver.prototxt -weights .\test.caffemodelpause Second, Caffe command full analysis Http://www.cnblogs.com/denny402/p/5076285.htmlPart Two: Example of tuning parameter adjustment

important aspect of the convnet architecture is design - depth. Many people try to improve the AlexNet proposed in the year to achieve better results , zfnet in the first convolution layer using smaller convolution ( Receptive window size) and smaller step size (Stride) 2, the other strategy is to intensively train and test the entire image on a multiscale scale. 2 convnet Configurationsbe Ciresan et al. (2011); Krizhevsky et al. (2012). Inspire.

1. Biologists have experimented with finding that the brain cortex responds to simple structures such as horns and edges, and through complex neurons, these simple structures ultimately help organisms to have more complex visual systems. 1970 David Marr's vision processing process follows the principle that after getting the image, it extracts simple geometric elements such as angles, edges, curves, and so on, and then uses more sophisticated information, such as depth information, surface infor

CNN (convolutional neural Network)Convolutional Neural Networks (CNN) dating back to the the 1960s, Hubel and others through the study of the cat's visual cortex cells show that the brain's access to information from the outside world is stimulated by a multi-layered receptive Field. On the basis of feeling wild, 1980 Fukushima proposed a theoretical model Neocognitron is the first application of the field of artificial neural network. In 1998, the LENET-5 model proposed by LeCun was successful

Today to see a more classical semantic segmentation network, that is FCN, full name title, the original English thesis website: https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdfThree big guys: Jonathan Long Evan shelhamer Trevor DarrellThis web site is a big guy on the Internet FCN blog, at the same time deeply felt the gap between himself and the big guy, but still have to bite the bullet to complete the paper, paste out the Web site, and we learn together:47205839To get to the p

translate the reason to say differently, the pool layer is a single layer , the pool layer is divided into the maximum pooling and the average value pooling; in fact, that is, the most obvious value of extracting eigenvalues, the maximum pooling effect is relatively better, so it is recommended to use.FC: That is all connected, namely Wx+b=ySoftmax: Normalization of treatment, classificationIn the process of conv2 to pool, there is a layer of no_linear_normal operations, but also difficult to u

Refer to "Deeplearning.ai convolutional neural Network Week 2 Lecture Notes".1. AlexNet (Krizhevsky et al. 2012), 8-layer network.Learn to calculate the shape of the output for each layer: for the convolution layer, the edge length of the output = (input side length-filter side length)/step + 1, the output number of channels equals the number of filter. The number of channels per filter equals the number of channels entered. The parameters of the conv

Preface CIFAR-10 datasets are a common data set in the field of deep learning. The Cifar-10 consists of 60000 32*32 RGB color images, all of which include aircraft, cars, birds, fur, deer, dogs, frogs, horses, boats and trucks in 10 categories. 50000 training, 10000 tests. is often used as a classification task to evaluate the merits and demerits of deep learning frameworks and models. More well-known models such as Alexnet, NIN, ResNet, etc. have al

. There is absolutely no need, and will cause the Spyder to start when the window, kernel died, and so on, this is my test, engaged a day ... "" When installing anaconda, do not install Python version 3.5, the total display GPU is not available. And do not install Spyder3 series, that is, more than Anaconda4.2.0. Instead, Python chooses 2.7,spyder to select the 2 series, which is the Anaconda4.1.1 version and below. What is the reason? Because Spyder3 always does not call the Ipythonw.exe interp

Sklearn.cluster.Birch Unknown K-medoids Clustering Pyclust. Kmedoids (Reliability unknown) Cluster.pam Association Rules category Python R Apriori algorithm Apriori (Unknown reliability, py3 not supported), Pyfim (Reliability unknown, PIP installation not available) Arules::apriori Fp-growth algorithm Fp-growth (Unknown reliability, py3 not supported), Pyfim (Reliability u

function, |a|>1, it means that the curve is getting smoother, Z-values tend to be closer to 1 or 0, which can also cause gradients to disappear.What if we can give a suitable value to W when we initialize the weights in each layer of the network, can we reduce the possibility of this gradient explosion or gradient disappearing? Let's see how to choose.One, random distribution weightsIn Keras, whose function is: k.random_uniform_variable (), let's tak

The problem is as follows:E:\project\dl\python\keras>python keras_sample.pyUsing Theano backend.Traceback (most recent):File "keras_sample.py", line 8, From Keras.preprocessing.image import ImagedatageneratorFile "D:\Program files\python_3.5\lib\site-packages\keras\preprocessing\image.py", line 9, From scipy import NdimageFile "D:\Program files\python_3.5\lib\site-packages\scipy\ndimage\__init__.py", line 1

Learning Goals Understand multiple foundational papers of convolutional neural networks Analyze the dimensionality reduction of a volume in a very deep network Understand and Implement a residual network Build a deep neural network using Keras Implement a skip-connection in your network Clone a repository from GitHub and use transfer learning Learning Goalsunderstanding of multi-basis papers in convolutional neural ne

. Typically, a gradient drop involves rolling down a hill in a static loss. But with Gan, every step down the hill will change the landscape. This is a dynamic system in which the optimization process seeks not the least, but a balance between two forces . For this reason, Gan is notoriously difficult to train -making Gan work requires a lot of careful adjustment of the model architecture and training parameters.Gan implementationUse Keras to impleme

accomplished by adding sigmoid activation to the last layer of decoder:F (x) =11+e−x as an example, we take M = 100,decoder for the most popular full connection network (MLP). The definitions based on the Keras functional API are as follows: N, m = 784, 2 Hidden_dim = 256 batch_size = M # # encoder z = Input (batch_shape= (Batch_size, M)) H_de coded = dense (Hidden_dim, activation= ' Tanh ') (z) x_hat = dense (n, activation= ' sigmoid ') (h_decoded)

2. CNN Reference URL: Https://github.com/Syndrome777/DeepLearningTutorial/blob/master/4_Convoltional_Neural_Networks_LeNet_%E5%8D%B7 %e7%a7%af%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c.md Http://www.cnblogs.com/charleshuang/p/3651843.html http://xilinx.eetrend.com/article/10863 Http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/deep_cnn.html http://www.lookfor404.com/tag/cnn/ Http://ufldl.stanford.edu/wiki/index.php/UFLDL%E6%95%99%E7%A8%8B Keras